Association of age with response to preoperative chemotherapy in patients with muscle-invasive bladder cancer
Bibliographic record
Abstract
PURPOSE: To assess the association of patient age with response to preoperative chemotherapy in patients with muscle-invasive bladder cancer (MIBC). MATERIALS AND METHODS: We analyzed data from 1105 patients with MIBC. Patients age was evaluated as continuous variable and stratified in quartiles. Pathologic objective response (pOR; ypT0-Ta-Tis-T1N0) and pathologic complete response (pCR; ypT0N0), as well survival outcomes were assessed. We used data of 395 patients from The Cancer Genome Atlas (TCGA) to investigate the prevalence of TCGA molecular subtypes and DNA damage repair (DDR) gene alterations according to patient age. RESULTS: pOR was achieved in 40% of patients. There was no difference in distribution of pOR or pCR between age quartiles. On univariable logistic regression analysis, patient age was not associated with pOR or pCR when evaluated as continuous variables or stratified in quartiles (all p > 0.3). Median follow-up was 18 months (IQR 6-37). On Cox regression and competing risk regression analyses, age was not associated with survival outcomes (all p > 0.05). In the TCGA cohort, patient with age ≤ 60 years has 7% less DDR gene mutations (p = 0.59). We found higher age distribution in patients with luminal (p < 0.001) and luminal infiltrated (p = 0.002) compared to those with luminal papillary subtype. CONCLUSIONS: While younger patients may have less mutational tumor burden, our analysis failed to show an association of age with response to preoperative chemotherapy or survival outcomes. Therefore, the use of preoperative chemotherapy should be considered regardless of patient age.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".